A Technical Review of the State-of-the-Art Methods in Aspect-Based Sentiment Analysis

Kabir Kasum Yusuf; Emeka Ogbuju; Taiwo Abiodun; Francisca Oladipo

doi:10.62411/jcta.9999

A Technical Review of the State-of-the-Art Methods in Aspect-Based Sentiment Analysis

Kabir Kasum Yusuf, Emeka Ogbuju, Taiwo Abiodun, Francisca Oladipo

Abstract

With the advent and rapid advancement of text mining technology, a computer-based approach used to capture sentiment standpoints from data in textual form is increasingly becoming a promising field. Detailed information about sentiment can be provided using aspect-based sentiment analysis, which can be used in better decision-making. This study aims to study, observe, and classify previous methods used in aspect-based sentiment analysis. A systematic review is adopted as the method used to collect and review papers to achieve this research's aim. Papers focused on sentiment analysis, aspect extraction, and aspect aggregation from different academic databases such as Scopus, ScienceDirect, IEEE Explore, and Web of Science were gathered based on the inclusion and exclusion criteria of the study. The gathered papers were further reviewed to answer the stated research questions. The findings from the research show the most used methods for aspect extraction, sentiment analysis, and aspect aggregation in aspect-based sentiment analysis. This research offers a robust synthesis of evidence to guide further academic exploration in sentiment analysis.

Keywords

Aspect-based sentiment analysis; Big data; Sentiment analysis; Systematic review; Text mining

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References

E. Ogbuju, F. Oladipo, V. Yemi-Petters, R. Abdumalik, T. Olowolafe, and A. Aliyu, “Sentiment Analysis of the Nigerian Nationwide Lockdown Due to COVID19 Outbreak,” SSRN Electron. J., 2020, doi: 10.2139/ssrn.3665975.

B. Liu, Sentiment Analysis and Opinion Mining. Cham: Springer International Publishing, 2012. doi: 10.1007/978-3-031-02145-9.

S. De, S. Dey, S. Bhattacharyya, and S. Bhatia, Eds., Advanced Data Mining Tools and Methods for Social Computing. Elsevier, 2022. doi: 10.1016/C2020-0-01963-8.

M. Tsytsarau and T. Palpanas, “Survey on mining subjective data on the web,” Data Min. Knowl. Discov., vol. 24, no. 3, pp. 478–514, May 2012, doi: 10.1007/s10618-011-0238-6.

G. Carenini, R. T. Ng, and E. Zwart, “Extracting knowledge from evaluative text,” in Proceedings of the 3rd international conference on Knowledge capture, Oct. 2005, pp. 11–18. doi: 10.1145/1088622.1088626.

D. Goodman and L. Deis, “Web of Science ( 2004 version ) and Scopus Composite Score : Scopus Reviewed by : David Goodman,” Charlest. Advis., no. January, 2005.

RELX Group, “Annual Reports and Financial Statements 2022,” 2023. [Online]. Available: https://www.relx.com/~/media/Files/R/RELX-Group/documents/reports/annual-reports/relx-2022-annual-report.pdf.

M. Wilde, “IEEE Xplore Digital Library,” Charlest. Advis., vol. 17, no. 4, pp. 24–30, Apr. 2016, doi: 10.5260/chara.17.4.24.

T. Berners-Lee, W. Hall, J. Hendler, N. Shadbolt, and D. J. Weitzner, “Creating a Science of the Web,” Science (80-. )., vol. 313, no. 5788, pp. 769–771, Aug. 2006, doi: 10.1126/science.1126902.

B. Kitchenham and S. M. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” 2007.

E. Marrese-Taylor, J. D. Velásquez, and F. Bravo-Marquez, “A novel deterministic approach for aspect-based opinion mining in tourism products reviews,” Expert Syst. Appl., vol. 41, no. 17, pp. 7764–7775, Dec. 2014, doi: 10.1016/j.eswa.2014.05.045.

R. Pradhan and D. K. Sharma, “A Frequency-Based Approach to Extract Aspect for Aspect-Based Sentiment Analysis,” in Lecture Notes in Networks and Systems, 2021, pp. 499–510. doi: 10.1007/978-981-16-0733-2_35.

A. Bagheri, M. Saraee, and F. de Jong, “Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews,” Knowledge-Based Syst., vol. 52, pp. 201–213, Nov. 2013, doi: 10.1016/j.knosys.2013.08.011.

N. Pathik and P. Shukla, “Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA,” J. Cases Inf. Technol., vol. 24, no. 3, pp. 1–19, Oct. 2021, doi: 10.4018/JCIT.20220701.oa3.

B. R. Bhamare and J. Prabhu, “A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas,” PeerJ Comput. Sci., vol. 7, p. e347, Feb. 2021, doi: 10.7717/peerj-cs.347.

J. Shi, W. Li, Q. Bai, and T. Ito, “BeeAE: effective aspect term extraction with artificial bee colony,” J. Supercomput., vol. 78, no. 16, pp. 17969–17991, Nov. 2022, doi: 10.1007/s11227-022-04579-0.

R. Piryani, V. Gupta, V. K. Singh, and U. Ghose, “A Linguistic Rule-Based Approach for Aspect-Level Sentiment Analysis of Movie Reviews,” 2017, pp. 201–209. doi: 10.1007/978-981-10-3770-2_19.

P. Ray and A. Chakrabarti, “A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis,” Appl. Comput. Informatics, vol. 18, no. 1/2, pp. 163–178, Mar. 2022, doi: 10.1016/j.aci.2019.02.002.

T. A. Rana and Y.-N. Cheah, “A two-fold rule-based model for aspect extraction,” Expert Syst. Appl., vol. 89, pp. 273–285, Dec. 2017, doi: 10.1016/j.eswa.2017.07.047.

M. Venugopalan, D. Gupta, and V. Bhatia, “A Supervised Approach to Aspect Term Extraction Using Minimal Robust Features for Sentiment Analysis,” 2021, pp. 237–251. doi: 10.1007/978-981-15-6353-9_22.

M. Dragoni, M. Federici, and A. Rexha, “An unsupervised aspect extraction strategy for monitoring real-time reviews stream,” Inf. Process. Manag., vol. 56, no. 3, pp. 1103–1118, May 2019, doi: 10.1016/j.ipm.2018.04.010.

N. Punetha and G. Jain, “Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews,” Appl. Intell., vol. 53, no. 17, pp. 20152–20173, Sep. 2023, doi: 10.1007/s10489-023-04471-1.

D. Mai and W. E. Zhang, “Aspect Extraction Using Coreference Resolution and Unsupervised Filtering,” in Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, Dec. 2020, pp. 124–129. [Online]. Available: https://aclanthology.org/2020.aacl-srw.18

D.-H. Pham and A.-C. Le, “Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis,” Int. J. Approx. Reason., vol. 103, pp. 1–10, Dec. 2018, doi: 10.1016/j.ijar.2018.08.003.

E. Ogbuju et al., “The Sentiment Analysis of EndSARS Protest in Nigeria,” J. Appl. Artif. Intell., vol. 3, no. 2, pp. 13–23, Dec. 2022, doi: 10.48185/jaai.v3i2.560.

H. S. Hota, D. K. Sharma, and N. Verma, “Lexicon-based sentiment analysis using Twitter data,” in Data Science for COVID-19, Elsevier, 2021, pp. 275–295. doi: 10.1016/B978-0-12-824536-1.00015-0.

R. L. Mustofa and B. Prasetiyo, “Sentiment analysis using lexicon-based method with naive bayes classifier algorithm on #newnormal hashtag in twitter,” J. Phys. Conf. Ser., vol. 1918, no. 4, p. 042155, Jun. 2021, doi: 10.1088/1742-6596/1918/4/042155.

M. M. Almosawi and S. A. Mahmood, “Lexicon-Based Approach For Sentiment Analysis To Student Feedback,” Webology, vol. 19, no. 1, pp. 6971–6989, 2022.

A. B. Muhammad and A. A. Dahiru, “Lexicon-based sentiment analysis of web discussion posts using SentiWordNet,” J. Comput. Sci. Its Appl., vol. 26, no. 2, p. 1, Feb. 2020, doi: 10.4314/jcsia.v26i2.1.

Y. Qi and Z. Shabrina, “Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach,” Soc. Netw. Anal. Min., vol. 13, no. 1, p. 31, Feb. 2023, doi: 10.1007/s13278-023-01030-x.

S. Naeem, D. Logof?tu, and F. Muharemi, “Sentiment Analysis by Using Supervised Machine Learning and Deep Learning Approaches,” in Communications in Computer and Information Science, 2020, pp. 481–491. doi: 10.1007/978-3-030-63119-2_39.

A. Mahajan, A. Ray, A. Verma, S. Kohad, and P. N. Thakare, “Sentiment Analysis using Supervised Machine Learning,” Int. J. Adv. Res. Innov. Ideas Educ., vol. 6, no. 6, pp. 103–109, 2020.

A. Salunkhe and S. Mhaske, “Aspect Based Sentiment Analysis on Financial Data using Transferred Learning Approach using Pre-Trained BERT and Regressor Model,” Int. Res. J. Eng. Technol., vol. 6, no. 12, pp. 1097–1101, 2019.

V. L. Shan Lee, K. H. Gan, T. P. Tan, and R. Abdullah, “Semi-supervised Learning for Sentiment Classification using Small Number of Labeled Data,” Procedia Comput. Sci., vol. 161, pp. 577–584, 2019, doi: 10.1016/j.procs.2019.11.159.

M. Ledwaba and V. Marivate, “Semi-supervised learning approaches for predicting South African political sentiment for local government elections,” in DG.O 2022: The 23rd Annual International Conference on Digital Government Research, Jun. 2022, pp. 129–137. doi: 10.1145/3543434.3543484.

J. J. E. Macrohon, C. N. Villavicencio, X. A. Inbaraj, and J.-H. Jeng, “A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential Election,” Information, vol. 13, no. 10, p. 484, Oct. 2022, doi: 10.3390/info13100484.

T. A. Rana, K. Shahzadi, T. Rana, A. Arshad, and M. Tubishat, “An Unsupervised Approach for Sentiment Analysis on Social Media Short Text Classification in Roman Urdu,” ACM Trans. Asian Low-Resource Lang. Inf. Process., vol. 21, no. 2, pp. 1–16, Mar. 2022, doi: 10.1145/3474119.

M. Bibi et al., “A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis,” Pattern Recognit. Lett., vol. 158, pp. 80–86, Jun. 2022, doi: 10.1016/j.patrec.2022.04.004.

O. Araque, G. Zhu, and C. A. Iglesias, “A semantic similarity-based perspective of affect lexicons for sentiment analysis,” Knowledge-Based Syst., vol. 165, pp. 346–359, Feb. 2019, doi: 10.1016/j.knosys.2018.12.005.

R. V. Sharapov, A. D. Varlamov, and E. V. Sharapova, “Method for Sentiment Text Analysis based on Statistical and Semantic Properties of Words,” in 2019 International Russian Automation Conference (RusAutoCon), Sep. 2019, pp. 1–6. doi: 10.1109/RUSAUTOCON.2019.8867775.

P. Somani and G. Kaur, “A review on Supervised Learning Algorithms,” Int. J. Adv. Sci. Technol., vol. 29, no. 10s, pp. 2551–2559, 2020.

S. Suhariyanto, R. Sarno, C. Fatihah, and E. Faisal, “Aspect Based Sentiment Analysis: A Systematic Literature Review,” J. Appl. Intell. Syst., vol. 5, no. 1, pp. 8–22, Dec. 2020, doi: 10.33633/jais.v5i1.3807.

M. Shams and A. Baraani-Dastjerdi, “Enriched LDA (ELDA): Combination of latent Dirichlet allocation with word co-occurrence analysis for aspect extraction,” Expert Syst. Appl., vol. 80, pp. 136–146, Sep. 2017, doi: 10.1016/j.eswa.2017.02.038.

Y. H. Tran and Q. N. Tran, “Estimating Public Opinion in Social Media Content Using Aspect-Based Opinion Mining,” in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2018, pp. 101–115. doi: 10.1007/978-3-319-90775-8_9.

A. Firmanto and R. Sarno, “Aspect-Based Sentiment Analysis Using Grammatical Rules, Word Similarity and SentiCircle,” Int. J. Intell. Eng. Syst., vol. 12, no. 5, pp. 190–201, Oct. 2019, doi: 10.22266/ijies2019.1031.19.

F. Nurifan, R. Sarno, and K. Sungkono, “Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle,” Int. J. Intell. Eng. Syst., vol. 12, no. 6, pp. 47–58, Dec. 2019, doi: 10.22266/ijies2019.1231.05.

N. Ayub, M. Ramzan Talib, M. Kashif Hanif, and M. Awais, “Aspect Extraction Approach for Sentiment Analysis Using Keywords,” Comput. Mater. Contin., vol. 74, no. 3, pp. 6879–6892, 2023, doi: 10.32604/cmc.2023.034214.

R. Priyantina and R. Sarno, “Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM,” Int. J. Intell. Eng. Syst., vol. 12, no. 4, pp. 142–155, Aug. 2019, doi: 10.22266/ijies2019.0831.14.

D. Khotimah and R. Sarno, “Sentiment Analysis of Hotel Aspect Using Probabilistic Latent Semantic Analysis, Word Embedding and LSTM,” Int. J. Intell. Eng. Syst., vol. 12, no. 4, pp. 275–290, Aug. 2019, doi: 10.22266/ijies2019.0831.26.

DOI: https://doi.org/10.62411/jcta.9999

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